Tao Zhu1, Deng-Ke Niu. 1. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China.
Abstract
BACKGROUND: Although intron loss in evolution has been described, the mechanism involved is still unclear. Three models have been proposed, the reverse transcriptase (RT) model, genomic deletion model and double-strand-break repair model. The RT model, also termed mRNA-mediated intron loss, suggests that cDNA molecules reverse transcribed from spliced mRNA recombine with genomic DNA causing intron loss. Many studies have attempted to test this model based on its predictions, such as simultaneous loss of adjacent introns, 3'-side bias of intron loss, and germline expression of intron-lost genes. Evidence either supporting or opposing the model has been reported. The mechanism of intron loss proposed in the RT model shares the process of reverse transcription with the formation of processed pseudogenes. If the RT model is correct, genes that have produced more processed pseudogenes are more likely to undergo intron loss. RESULTS: In the present study, we observed that the frequency of intron loss is correlated with processed pseudogene abundance by analyzing a new dataset of intron loss obtained in mice and rats. Furthermore, we found that mRNA molecules of intron-lost genes are mostly translated on free cytoplasmic ribosomes, a feature shared by mRNA molecules of the parental genes of processed pseudogenes and long interspersed elements. This feature is likely convenient for intron-lost gene mRNA molecules to be reverse transcribed. Analyses of adjacent intron loss, 3'-side bias of intron loss, and germline expression of intron-lost genes also support the RT model. CONCLUSIONS: Compared with previous evidence, the correlation between the abundance of processed pseudogenes and intron loss frequency more directly supports the RT model of intron loss. Exploring such a correlation is a new strategy to test the RT model in organisms with abundant processed pseudogenes.
BACKGROUND: Although intron loss in evolution has been described, the mechanism involved is still unclear. Three models have been proposed, the reverse transcriptase (RT) model, genomic deletion model and double-strand-break repair model. The RT model, also termed mRNA-mediated intron loss, suggests that cDNA molecules reverse transcribed from spliced mRNA recombine with genomic DNA causing intron loss. Many studies have attempted to test this model based on its predictions, such as simultaneous loss of adjacent introns, 3'-side bias of intron loss, and germline expression of intron-lost genes. Evidence either supporting or opposing the model has been reported. The mechanism of intron loss proposed in the RT model shares the process of reverse transcription with the formation of processed pseudogenes. If the RT model is correct, genes that have produced more processed pseudogenes are more likely to undergo intron loss. RESULTS: In the present study, we observed that the frequency of intron loss is correlated with processed pseudogene abundance by analyzing a new dataset of intron loss obtained in mice and rats. Furthermore, we found that mRNA molecules of intron-lost genes are mostly translated on free cytoplasmic ribosomes, a feature shared by mRNA molecules of the parental genes of processed pseudogenes and long interspersed elements. This feature is likely convenient for intron-lost gene mRNA molecules to be reverse transcribed. Analyses of adjacent intron loss, 3'-side bias of intron loss, and germline expression of intron-lost genes also support the RT model. CONCLUSIONS: Compared with previous evidence, the correlation between the abundance of processed pseudogenes and intron loss frequency more directly supports the RT model of intron loss. Exploring such a correlation is a new strategy to test the RT model in organisms with abundant processed pseudogenes.
Authors: France Denoeud; Simon Henriet; Sutada Mungpakdee; Jean-Marc Aury; Corinne Da Silva; Henner Brinkmann; Jana Mikhaleva; Lisbeth Charlotte Olsen; Claire Jubin; Cristian Cañestro; Jean-Marie Bouquet; Gemma Danks; Julie Poulain; Coen Campsteijn; Marcin Adamski; Ismael Cross; Fekadu Yadetie; Matthieu Muffato; Alexandra Louis; Stephen Butcher; Georgia Tsagkogeorga; Anke Konrad; Sarabdeep Singh; Marit Flo Jensen; Evelyne Huynh Cong; Helen Eikeseth-Otteraa; Benjamin Noel; Véronique Anthouard; Betina M Porcel; Rym Kachouri-Lafond; Atsuo Nishino; Matteo Ugolini; Pascal Chourrout; Hiroki Nishida; Rein Aasland; Snehalata Huzurbazar; Eric Westhof; Frédéric Delsuc; Hans Lehrach; Richard Reinhardt; Jean Weissenbach; Scott W Roy; François Artiguenave; John H Postlethwait; J Robert Manak; Eric M Thompson; Olivier Jaillon; Louis Du Pasquier; Pierre Boudinot; David A Liberles; Jean-Nicolas Volff; Hervé Philippe; Boris Lenhard; Hugues Roest Crollius; Patrick Wincker; Daniel Chourrout Journal: Science Date: 2010-11-18 Impact factor: 47.728
Authors: Argelia Cuenca; T Gregory Ross; Sean W Graham; Craig F Barrett; Jerrold I Davis; Ole Seberg; Gitte Petersen Journal: Genome Biol Evol Date: 2016-08-03 Impact factor: 3.416